AI and Autonomous Systems for Assessing Biodiversity and Ecosystem Health

We will leverage connections between partners and key organisations to promote a Turing-led vision for AI-driven biodiversity monitoring , through existing events, new activities  and (inter)national initiatives, engaging with industry (instrumentation / software developers, water companies), government (Defra and Environment Agency as national monitoring bodies), research (HEIs, funders, data centres), and 3rd sector (citizen science networks). 
 
Addressing outstanding technical challenges will demonstrate how AI research can deliver solutions for biodiversity monitoring:  

Plankton plays a critical role in indicating the health of aquatic ecosystems due to its sensitivity to environmental changes and short life cycles. The advent of AI technologies and high-speed imaging instruments offers a promising avenue to enhance the accuracy and efficiency of plankton monitoring. We are utilising data from the  Plankton Imaging (Pi-10) instrument deployed on the  RV Cefas Endeavour to develop a near real-time monitoring application using SOTA AI techniques. 

Building on the work of the Turing Data Study Group (DSG), we aim to improve the DSG classifier for real-time access to plankton imager data (RAPID), a suite of tools designed for use with the Plankton Analytics Pi-10 instrument. The first version of the RAPID Plankton software was operational within six months and deployed on the RV Cefas Endeavour. This open-source software can be adapted to classify images of various marine objects and species, revolutionizing how scientists study the oceans. Further developments in this project include the deployment of a dashboard, which provides real-time plankton metrics. Additionally, we are developing explainable AI (xAI) interpretations to collaborate with ecologists, helping them understand how AI works and ensuring the AI models are transparent and interpretable. 

We are developing new AI methods that can meet the demand for resource-efficient terrestrial biodiversity monitoring, by integrating two data sources available at scale: remote sensing and citizen-science data. Remote sensing data, such as satellite imagery, have wide coverage and are routinely used for mapping habitats. At the same time, wildlife records collected on the ground by volunteers give high-quality, but local and resource-intensive, measurements of habitat quality. For example, some butterflies are only found in healthy grasslands, and are therefore good indicators of ecosystem health.  

As part of this research, we aim to bridge the scale from local recordings to global satellite coverage, by developing a predictive model of species biodiversity based on satellite data. This model can be used for locating high-biodiversity habitats, at high spatial and temporal resolution in a standardised way. This is important as more nature needs to be protected to reach the UN’s goal of protecting 30% of land and sea by 2030.